QAIS Implementation Program
Enterprise implementation of Quantitative AI Security (QAIS) Framework
4
12 months
2025-02-01
2026-01-31
Establish comprehensive AI asset inventory and initial QAIS scoring
Duration: 4 weeks
Start Date: 2025-02-01
End Date: 2025-02-28
Epics
Complete discovery of all ML/AI assets across the organization
User Stories
As a Security Analyst, I want automated discovery of all training datasets and data pipelines so that I can establish complete data sanctity baseline
Story Points: 13
Acceptance Criteria
All datasets catalogued with metadata
Data lineage tracked for 95% of assets
PII/PHI detection automated
Classification by sensitivity completed
Tasks
Deploy Apache Atlas for data lineage
Install and configure Apache Atlas for automated data discovery
Effort: 3 days
Subtasks
Install Apache Atlas on Kubernetes
Feature: Apache Atlas Deployment
Successful Atlas deployment
Kubernetes cluster with sufficient resources
Helm chart is deployed with security configurations
• Atlas services are running
• UI is accessible via secure endpoint
• Authentication is integrated with SSO
Configure data source connectors
Feature: Data Source Integration
Connect to cloud storage
S3, Azure Blob, and GCS credentials configured
Connectors are activated with scanning schedule
• All buckets are discovered
• Data assets are automatically catalogued
• Metadata extraction completes within SLA
Implement PII/PHI scanning
Deploy automated sensitive data detection
Effort: 2 days
Subtasks
Configure Microsoft Purview scanning
Feature: Sensitive Data Detection
Detect PII in datasets
Dataset contains mixed data types
Purview scan is executed
• PII fields are identified with 99% accuracy
• Compliance tags are applied
• Risk score is calculated
As a ML Engineer, I want centralized registry of all ML models so that we can track model versions and security posture
Story Points: 8
Acceptance Criteria
All production models registered
Model metadata includes security attributes
Version control integrated
API endpoints documented
Tasks
Deploy MLflow Model Registry
Set up centralized model management
Effort: 2 days
Subtasks
Install MLflow with security hardening
Feature: MLflow Security Configuration
Secure MLflow deployment
MLflow server with PostgreSQL backend
Security configurations are applied
• RBAC is enforced
• Model artifacts are encrypted
• Audit logging is enabled
Establish baseline security metrics using QAIS methodology
User Stories
As a Security Champion, I want automated QAIS scoring for critical AI assets so that we have quantitative baseline metrics
Story Points: 13
Acceptance Criteria
DSS, MRS, IHS scores calculated
Aggregate QAIS score using harmonic mean
Dashboard visualization available
Historical tracking enabled
Tasks
Implement Data Sanctity Score calculator
Build DSS scoring engine
Effort: 3 days
Subtasks
Calculate provenance score
Feature: Data Provenance Scoring
Score data with complete lineage
Dataset with full lineage documentation
DSS calculation is triggered
• Provenance subscore equals 0.95
• Cryptographic hashes verified
• Score persisted to database
As a Security Architect, I want comprehensive threat models for AI systems so that we identify all potential attack vectors
Story Points: 8
Acceptance Criteria
All 7 STRIDE-LM categories assessed
Attack trees documented
Risk scores calculated
Mitigation strategies defined
Tasks
Conduct threat modeling workshops
Facilitate STRIDE-LM analysis sessions
Effort: 2 days
Subtasks
Analyze Learning Manipulation threats
Feature: LM Threat Identification
Identify data poisoning vectors
Training pipeline architecture
LM analysis is performed
• Poisoning entry points identified
• Attack likelihood scored
• Impact assessment completed
Implement high-priority controls on pilot systems
Duration: 6 weeks
Start Date: 2025-03-01
End Date: 2025-04-11
Epics
Deploy defensive controls for data, models, and infrastructure
User Stories
As a Data Engineer, I want automated data validation and anomaly detection so that we prevent data poisoning attacks
Story Points: 13
Acceptance Criteria
Statistical outlier detection operational
Provenance tracking automated
Alert thresholds configured
False positive rate < 5%
Tasks
Deploy isolation forest anomaly detection
Implement statistical outlier detection
Effort: 3 days
Subtasks
Train isolation forest model
Feature: Anomaly Detection Training
Train on baseline data
Clean training dataset
Isolation forest is trained
• Model achieves 0.98 AUC
• Contamination factor optimized
• Model serialized and versioned
As a ML Engineer, I want adversarial training integrated in pipeline so that models resist evasion attacks
Story Points: 21
Acceptance Criteria
IBM ART integrated
PGD, FGSM, C&W attacks implemented
Robustness metrics tracked
Performance degradation < 15%
Tasks
Integrate IBM Adversarial Robustness Toolbox
Deploy ART for adversarial training
Effort: 5 days
Subtasks
Configure PGD adversarial training
Feature: PGD Training Integration
Train with PGD augmentation
Base model and training data
PGD adversarial examples generated
• Model trained on augmented dataset
• Robustness score increases by 40%
• Clean accuracy maintained above 85%
As a Privacy Engineer, I want differential privacy in model training so that we prevent membership inference attacks
Story Points: 13
Acceptance Criteria
TensorFlow Privacy deployed
Epsilon budget tracked
Privacy loss calculated
Utility-privacy tradeoff optimized
Tasks
Implement DP-SGD training
Add differential privacy to training
Effort: 3 days
Subtasks
Configure privacy budget
Feature: Privacy Budget Management
Set epsilon for healthcare data
PHI dataset requiring protection
DP-SGD is configured
• Epsilon set to 1.0
• Delta set to 1e-5
• Privacy accounting enabled
Establish monitoring and threat hunting capabilities
User Stories
As a Security Operations, I want centralized AI security event logging so that we can perform forensics and threat hunting
Story Points: 13
Acceptance Criteria
Schema supports AI-specific events
Real-time streaming enabled
90-day retention configured
SIEM integration complete
Tasks
Deploy Elasticsearch for AI events
Set up security data lake infrastructure
Effort: 3 days
Subtasks
Define AI event schema
Feature: AI Event Schema
Log adversarial detection event
Adversarial input detected
Event is logged
• Model ID captured
• Input features stored
• Confidence scores recorded
• Timestamp and context preserved
As a Red Team Lead, I want AI-specific attack scenarios so that we can proactively test defenses
Story Points: 8
Acceptance Criteria
20+ attack scenarios documented
Automated testing framework built
Success metrics defined
Remediation playbooks created
Tasks
Create evasion attack scenarios
Document adversarial example attacks
Effort: 2 days
Subtasks
Implement FGSM attack test
Feature: FGSM Attack Simulation
Execute FGSM against image classifier
Production image classification model
FGSM perturbation applied
• Attack success rate measured
• Detection systems triggered
• Incident response activated
Deploy QAIS on selected pilot systems
User Stories
As a Program Manager, I want representative AI systems for pilot so that we validate QAIS implementation
Story Points: 5
Acceptance Criteria
3 pilot systems selected
Baseline metrics captured
Success criteria defined
Stakeholders aligned
Tasks
Configure pilot monitoring
Set up comprehensive monitoring for pilots
Effort: 2 days
Subtasks
Deploy Evidently AI monitoring
Feature: Pilot Monitoring Setup
Monitor data drift in pilot
Production model with baseline
Evidently monitoring activated
• Drift detected within 1 hour
• Alerts sent to team
• Dashboard updated real-time
Scale QAIS implementation across all AI systems
Duration: 8 weeks
Start Date: 2025-04-12
End Date: 2025-06-06
Epics
Embed QAIS controls in CI/CD pipelines
User Stories
As a DevOps Engineer, I want security validation in CI/CD so that insecure models cannot deploy
Story Points: 13
Acceptance Criteria
Pre-commit hooks configured
Build-time security scanning
Automated QAIS scoring
Deployment gates enforce thresholds
Tasks
Implement GitLab CI security pipeline
Add security stages to CI/CD
Effort: 3 days
Subtasks
Configure QAIS scoring job
Feature: CI/CD Security Gate
Block deployment of insecure model
Model with QAIS score below 6.0
Deployment pipeline executes
• Security gate fails
• Detailed report generated
• Team notified with remediation steps
As a Platform Engineer, I want autonomous security agents deployed so that continuous protection is maintained
Story Points: 21
Acceptance Criteria
Bandroid agents on all nodes
Real-time threat detection active
Automated response configured
Central management console operational
Tasks
Deploy Bandroid agent fleet
Install and configure autonomous agents
Effort: 5 days
Subtasks
Configure agent behavioral policies
Feature: Bandroid Agent Configuration
Agent detects adversarial input
Bandroid agent monitoring inference
Adversarial pattern detected
• Input quarantined within 100ms
• Alert sent to SOC
• Defensive measures activated
• Forensic data captured
Deploy unified security orchestration platform
User Stories
As a Security Architect, I want centralized security orchestration so that all QAIS components integrate seamlessly
Story Points: 21
Acceptance Criteria
ONE platform deployed on Kubernetes
All Bandroid agents connected
QAIS scoring integrated
Executive dashboard operational
Tasks
Deploy ONE orchestration engine
Install core ONE platform components
Effort: 5 days
Subtasks
Configure policy engine
Feature: ONE Policy Engine
Enforce QAIS threshold policy
Policy requiring QAIS > 7.0
Model deployment requested
• Current score evaluated
• Policy decision made
• Audit trail created
• Enforcement action taken
As a SOC Analyst, I want automated incident response so that AI security incidents are handled efficiently
Story Points: 13
Acceptance Criteria
Playbooks for 15 vulnerability patterns
Automated containment actions
Integration with ticketing system
SLA compliance tracking
Tasks
Create incident response playbooks
Develop automated response workflows
Effort: 3 days
Subtasks
Data poisoning response playbook
Feature: Poisoning Incident Response
Respond to detected poisoning
Data poisoning alert triggered
SOAR playbook executes
• Affected data quarantined
• Model rollback initiated
• Root cause analysis started
• Stakeholders notified
Enable organization-wide QAIS adoption
User Stories
As a Training Manager, I want certified AI Security Champions so that each team has embedded expertise
Story Points: 8
Acceptance Criteria
Training curriculum developed
20+ champions trained
Certification process established
Community of practice active
Tasks
Develop champion training program
Create comprehensive training materials
Effort: 3 days
Subtasks
Create hands-on labs
Feature: Champion Training Lab
Complete adversarial training lab
Champion accessing lab environment
Lab exercises completed
• Adversarial examples generated
• Model retrained with ART
• Robustness improvement measured
• Certificate of completion issued
Maintain and evolve QAIS implementation
Duration: 26 weeks
Start Date: 2025-06-07
End Date: 2025-12-05
Epics
Implement next-generation AI security capabilities
User Stories
As a AI Security Engineer, I want prompt injection defenses so that LLMs are protected from manipulation
Story Points: 13
Acceptance Criteria
Input sanitization implemented
Prompt firewall deployed
Jailbreak detection active
Output filtering configured
Tasks
Deploy prompt injection detection
Implement multi-layer prompt security
Effort: 3 days
Subtasks
Configure prompt firewall rules
Feature: Prompt Injection Defense
Block malicious prompt
User submits injection attempt
Prompt analyzed by firewall
• Injection pattern detected
• Request blocked
• Incident logged
• User warned
As a Verification Engineer, I want mathematical guarantees for AI safety so that critical systems have proven properties
Story Points: 21
Acceptance Criteria
Marabou framework integrated
Robustness certificates generated
Safety properties verified
Compliance documentation automated
Tasks
Implement robustness certification
Deploy formal verification tools
Effort: 5 days
Subtasks
Generate robustness certificates
Feature: Robustness Certification
Certify model robustness
Trained neural network
Formal verification executed
• L2 robustness bound calculated
• Certificate generated
• Bounds validated empirically
• Report published
Measure and improve QAIS effectiveness
User Stories
As a CISO, I want executive AI security dashboard so that I can track security posture trends
Story Points: 8
Acceptance Criteria
Real-time QAIS scores displayed
Incident trends visualized
ROI metrics calculated
Compliance status shown
Tasks
Build Grafana dashboards
Create executive security views
Effort: 2 days
Subtasks
Configure QAIS trend panels
Feature: Executive Dashboard
Display QAIS score trends
Historical QAIS data available
Dashboard loads
• 30-day trend displayed
• Tower breakdowns shown
• Risk heatmap rendered
• Drill-down enabled